Spatio-Temporal Dynamics and Haze Agglomeration Analysis in the Beijing-Tianjin-Hebei Region: A WOA-LSTM Approach DOI Creative Commons
Yuanyuan Wang, Zhuang Wu, Jiaqi Du

et al.

Journal of Urban Development and Management, Journal Year: 2023, Volume and Issue: 2(3), P. 145 - 159

Published: Sept. 30, 2023

The intensification of industrial and urban growth has precipitated a significant increase in atmospheric pollutant emissions, thereby exacerbating air quality deterioration. This phenomenon is particularly pronounced within the Beijing-Tianjin-Hebei agglomeration, where haze events have manifested with increasing frequency. Prior investigations predominantly concentrated on temporal trends, often overlooking critical impact geographical factors development. research delves into spatio-temporal distribution traits region, employing Whale Optimization Algorithm-Long Short-Term Memory (WOA-LSTM) model. Findings indicate spatial concentration pollution region's southern sector. In terms distribution, Air Quality Index (AQI) demonstrates distinct seasonal fluctuations, highest levels recorded winter notably lower observed during summer. study's innovation lies development WOA-LSTM model, which not only predicts AQI - comprehensive index but also offers early warnings pertinent to public travel. By integrating extensive datasets applying advanced analytical techniques, study contributes significantly understanding complex interplay between dynamics distribution. underscores necessity for regional policies tailored specific spatiotemporal characteristics, aiding effective management mitigation strategies agglomerations.

Language: Английский

Temporal and spatial variation characteristics of major air pollutants in Shanghai from 2019 to 2022 DOI Creative Commons

Chaoyi Zhai,

Lian Duan

E3S Web of Conferences, Journal Year: 2024, Volume and Issue: 554, P. 01010 - 01010

Published: Jan. 1, 2024

To inlustrate the spatio-temporal distribution and trends of six major air pollutants (PM2.5, PM10, O 3 , NO 2 SO CO), a comprehensive analysis atmospheric pollution data in Shanghai from 2019 to 2022 was conducted. The results showed that all pollutant except decreased yearly, with PM2.5 experiencing roughly 24.3% decrease showing reduction approximately 35.8% 2022. However, concentrations exhibited significant increase 2022, rising by 13.1% compared 2021. Seasonal variations indicate severe ozone summer particulate matter autumn winter. Spatial characteristics highlight higher western regions eastern regions, possibly linked predominant wind directions source distribution. Correlation studies strong positive correlation between PM10 Shanghai’s atmosphere, while pronounced negative exists NO2. In January prevailing airflow northeast, transported southward, adversely elevating other for month. May both east southwest likely its precursors diverse sources Shanghai, aiding explaining elevated concentration during

Language: Английский

Citations

0
Spatio-Temporal Dynamics and Haze Agglomeration Analysis in the Beijing-Tianjin-Hebei Region: A WOA-LSTM Approach DOI Creative Commons
Yuanyuan Wang, Zhuang Wu, Jiaqi Du

et al.

Journal of Urban Development and Management, Journal Year: 2023, Volume and Issue: 2(3), P. 145 - 159

Published: Sept. 30, 2023

The intensification of industrial and urban growth has precipitated a significant increase in atmospheric pollutant emissions, thereby exacerbating air quality deterioration. This phenomenon is particularly pronounced within the Beijing-Tianjin-Hebei agglomeration, where haze events have manifested with increasing frequency. Prior investigations predominantly concentrated on temporal trends, often overlooking critical impact geographical factors development. research delves into spatio-temporal distribution traits region, employing Whale Optimization Algorithm-Long Short-Term Memory (WOA-LSTM) model. Findings indicate spatial concentration pollution region's southern sector. In terms distribution, Air Quality Index (AQI) demonstrates distinct seasonal fluctuations, highest levels recorded winter notably lower observed during summer. study's innovation lies development WOA-LSTM model, which not only predicts AQI - comprehensive index but also offers early warnings pertinent to public travel. By integrating extensive datasets applying advanced analytical techniques, study contributes significantly understanding complex interplay between dynamics distribution. underscores necessity for regional policies tailored specific spatiotemporal characteristics, aiding effective management mitigation strategies agglomerations.

Language: Английский

Citations

0